BPI: Descriptive analyses
Peter Kamerman, Tory Madden, and Antonia Wadley
25 October 2017
Import data
# Read in bpi data
bpi <- readr::read_rds('./data/bpi.rds') %>%
arrange(ID) %>%
mutate(ID = stringr::str_to_lower(ID))
# Read in demographics.rds for site, group, and sex info
foo <- readr::read_rds('./data/demographics.rds') %>%
select(ID, Site, Group, Sex) %>%
rename(ID2 = ID) %>%
arrange(ID2)
# Join the two datasets
bpi <- foo %>%
bind_cols(bpi) %>%
rowwise() %>%
mutate(ID_match = grepl(pattern = ID2,
x = ID))
# Check for mismatches from join
nrow(filter(bpi, ID_match == FALSE))
# Clean-up columns
bpi <- bpi %>%
select(-ID,
-ID_match) %>%
rename(ID = ID2)Quick look
glimpse(bpi)## Observations: 160
## Variables: 88
## $ ID <chr> "j1", "j10", "j11", "j12", "j17", "j18", "j19",...
## $ Site <chr> "u1", "u1", "u1", "u1", "u1", "u1", "u1", "u1",...
## $ Group <chr> "p", "t", "p", "t", "t", "t", "t", "p", "t", "t...
## $ Sex <chr> "female", "male", "female", "female", "female",...
## $ Pain_Pres_BL <chr> "Yes", "Yes", "Yes", "Yes", "Yes", NA, "Yes", "...
## $ Pain_Pres_Wk4 <chr> NA, "Yes", "Yes", NA, NA, NA, "Yes", NA, "Yes",...
## $ Pain_Pres_Wk8 <chr> NA, "Yes", "Yes", "Yes", NA, NA, "Yes", "Yes", ...
## $ Pain_Pres_Wk12 <chr> NA, "Yes", "Yes", "Yes", "Yes", NA, NA, NA, "Ye...
## $ Pain_Pres_Wk24 <chr> NA, "Yes", NA, "Yes", NA, NA, NA, "Yes", "Yes",...
## $ Pain_Pres_Wk48 <chr> NA, "Yes", NA, "Yes", NA, NA, NA, "Yes", NA, "Y...
## $ Worst_BL <dbl> 8, 10, 9, 0, 10, NA, 9, 8, 10, 6, NA, 9, 10, 6,...
## $ Worst_Wk4 <dbl> NA, 9, 8, NA, NA, NA, 8, NA, 10, 8, NA, NA, NA,...
## $ Worst_Wk8 <dbl> NA, 9, 10, 4, NA, NA, 9, 6, 7, 7, NA, NA, NA, N...
## $ Worst_Wk12 <dbl> NA, 9, 10, 7, 10, NA, NA, NA, 7, 8, NA, NA, NA,...
## $ Worst_Wk24 <dbl> NA, 9, NA, 6, NA, NA, NA, 9, 5, 8, NA, NA, NA, ...
## $ Worst_Wk48 <dbl> NA, 8, NA, 8, NA, NA, NA, 6, NA, 8, NA, NA, NA,...
## $ Least_BL <dbl> 4, 5, 3, 0, 3, NA, 3, 3, 2, 3, NA, 1, 4, 2, 2, ...
## $ Least_Wk4 <dbl> NA, 4, 2, NA, NA, NA, 3, NA, 3, 3, NA, NA, NA, ...
## $ Least_Wk8 <dbl> NA, 4, 5, 1, NA, NA, 4, 3, 3, 3, NA, NA, NA, NA...
## $ Least_Wk12 <dbl> NA, 6, 5, 4, 5, NA, NA, NA, 3, 3, NA, NA, NA, N...
## $ Least_Wk24 <dbl> NA, 5, NA, 2, NA, NA, NA, 7, 2, 3, NA, NA, NA, ...
## $ Least_Wk48 <dbl> NA, 5, NA, 2, NA, NA, NA, 2, NA, 4, NA, NA, NA,...
## $ Avg_BL <dbl> 4, 7, 6, 0, 6, NA, 6, 6, 5, 5, NA, 4, 6, 4, 3, ...
## $ Avg_Wk4 <dbl> NA, 6, 5, NA, NA, NA, 5, NA, 5, 5, NA, NA, NA, ...
## $ Avg_Wk8 <dbl> NA, 7, 8, 2, NA, NA, 6, 5, 4, 5, NA, NA, NA, NA...
## $ Avg_Wk12 <dbl> NA, 7, 7, 6, 8, NA, NA, NA, 5, 5, NA, NA, NA, N...
## $ Avg_Wk24 <dbl> NA, 7, NA, 4, NA, NA, NA, 8, 3, 6, NA, NA, NA, ...
## $ Avg_Wk48 <dbl> NA, 7, NA, 5, NA, NA, NA, 4, NA, 6, NA, NA, NA,...
## $ Now_BL <dbl> 6, 0, 9, 0, 3, NA, 0, 8, 6, 3, NA, 4, 10, 6, 1,...
## $ Now_Wk4 <dbl> NA, 4, 6, NA, NA, NA, 0, NA, 0, 5, NA, NA, NA, ...
## $ Now_Wk8 <dbl> NA, 5, 10, 1, NA, NA, 0, 7, 4, 3, NA, NA, NA, N...
## $ Now_Wk12 <dbl> NA, 5, 10, 3, 8, NA, NA, NA, 5, 8, NA, NA, NA, ...
## $ Now_Wk24 <dbl> NA, 8, NA, 3, NA, NA, NA, 9, 2, 8, NA, NA, NA, ...
## $ Now_Wk48 <dbl> NA, 2, NA, 5, NA, NA, NA, 2, NA, 2, NA, NA, NA,...
## $ Rx_BL <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ Rx_Wk4 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ Rx_Wk8 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ Rx_Wk12 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ Rx_Wk24 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ Rx_Wk48 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ Relief_BL <dbl> 0, 0, 30, 70, NA, NA, 70, 0, 40, 50, NA, 0, 10,...
## $ Relief_Wk4 <dbl> NA, 0, 30, NA, NA, NA, 40, NA, 80, 20, NA, NA, ...
## $ Relief_Wk8 <dbl> NA, 0, NA, 70, NA, NA, 70, NA, 60, 50, NA, NA, ...
## $ Relief_Wk12 <dbl> NA, 0, 10, 50, 10, NA, NA, NA, 50, 20, NA, NA, ...
## $ Relief_Wk24 <dbl> NA, 0, NA, 30, NA, NA, NA, NA, 70, 10, NA, NA, ...
## $ Relief_Wk48 <dbl> NA, 40, NA, 40, NA, NA, NA, NA, NA, 30, NA, NA,...
## $ Activ_BL <dbl> 6, 0, 10, 0, 7, NA, 7, 7, 6, 4, NA, 9, 10, 5, 5...
## $ Activ_Wk4 <dbl> NA, 9, 0, NA, NA, NA, 2, NA, 2, 3, NA, NA, NA, ...
## $ Activ_Wk8 <dbl> NA, 8, 10, 4, NA, NA, 0, 6, 5, 0, NA, NA, NA, N...
## $ Activ_Wk12 <dbl> NA, 8, 10, 7, 10, NA, NA, NA, 5, 7, NA, NA, NA,...
## $ Activ_Wk24 <dbl> NA, 5, NA, 7, NA, NA, NA, 8, 5, 7, NA, NA, NA, ...
## $ Activ_Wk48 <dbl> NA, 6, NA, 7, NA, NA, NA, 7, NA, 8, NA, NA, NA,...
## $ Mood_BL <dbl> 8, 8, 0, 0, 4, NA, 9, 10, 5, 0, NA, 10, 5, 10, ...
## $ Mood_Wk4 <dbl> NA, 6, 0, NA, NA, NA, 0, NA, 3, 5, NA, NA, NA, ...
## $ Mood_Wk8 <dbl> NA, 7, 3, 8, NA, NA, 0, 7, 3, 0, NA, NA, NA, NA...
## $ Mood_Wk12 <dbl> NA, 4, 10, 8, 10, NA, NA, NA, 4, 8, NA, NA, NA,...
## $ Mood_Wk24 <dbl> NA, 6, NA, 8, NA, NA, NA, 10, 5, 3, NA, NA, NA,...
## $ Mood_Wk48 <dbl> NA, 5, NA, 8, NA, NA, NA, 6, NA, 7, NA, NA, NA,...
## $ Walking_BL <dbl> 4, 0, 7, 0, 8, NA, 0, 4, 6, 3, NA, 10, 9, 2, 0,...
## $ Walking_Wk4 <dbl> NA, 0, 0, NA, NA, NA, 4, NA, 4, 4, NA, NA, NA, ...
## $ Walking_Wk8 <dbl> NA, 7, 7, 0, NA, NA, 0, 7, 3, 4, NA, NA, NA, NA...
## $ Walking_Wk12 <dbl> NA, 7, 10, 0, 10, NA, NA, NA, 2, 10, NA, NA, NA...
## $ Walking_Wk24 <dbl> NA, 4, NA, 0, NA, NA, NA, 7, 5, 8, NA, NA, NA, ...
## $ Walking_Wk48 <dbl> NA, 4, NA, 3, NA, NA, NA, 3, NA, 8, NA, NA, NA,...
## $ Work_BL <dbl> 5, 0, 8, 0, 6, NA, 5, 7, 6, 2, NA, 8, 10, 10, 6...
## $ Work_Wk4 <dbl> NA, 3, 0, NA, NA, NA, 4, NA, 4, 4, NA, NA, NA, ...
## $ Work_Wk8 <dbl> NA, 3, 10, 6, NA, NA, 0, 10, 3, 0, NA, NA, NA, ...
## $ Work_Wk12 <dbl> NA, 4, 10, 7, 8, NA, NA, NA, 4, 6, NA, NA, NA, ...
## $ Work_Wk24 <dbl> NA, 5, NA, 4, NA, NA, NA, 8, 1, 7, NA, NA, NA, ...
## $ Work_Wk48 <dbl> NA, 5, NA, 5, NA, NA, NA, 5, NA, 8, NA, NA, NA,...
## $ Rel_BL <dbl> 4, 5, 0, 0, 7, NA, 9, 4, 5, 0, NA, 10, 6, 6, NA...
## $ Rel_Wk4 <dbl> NA, 0, 0, NA, NA, NA, 0, NA, 2, 0, NA, NA, NA, ...
## $ Rel_Wk8 <dbl> NA, 4, 6, 6, NA, NA, 0, 9, 3, 0, NA, NA, NA, NA...
## $ Rel_Wk12 <dbl> NA, 4, 7, 8, 7, NA, NA, NA, 2, 4, NA, NA, NA, N...
## $ Rel_Wk24 <dbl> NA, 6, NA, 5, NA, NA, NA, 10, 5, 3, NA, NA, NA,...
## $ Rel_Wk48 <dbl> NA, 6, NA, 6, NA, NA, NA, 5, NA, 7, NA, NA, NA,...
## $ Sleep_BL <dbl> 2, 8, 10, 0, 0, NA, 0, 6, 0, 0, NA, 10, 10, 7, ...
## $ Sleep_Wk4 <dbl> NA, 6, 10, NA, NA, NA, 0, NA, 4, 0, NA, NA, NA,...
## $ Sleep_Wk8 <dbl> NA, 0, 10, 3, NA, NA, 6, 0, 3, 0, NA, NA, NA, N...
## $ Sleep_Wk12 <dbl> NA, 6, 10, 0, 10, NA, NA, NA, 0, 0, NA, NA, NA,...
## $ Sleep_Wk24 <dbl> NA, 4, NA, 0, NA, NA, NA, 10, 1, 1, NA, NA, NA,...
## $ Sleep_Wk48 <dbl> NA, 7, NA, 5, NA, NA, NA, 2, NA, 7, NA, NA, NA,...
## $ Enjoy_BL <dbl> 3, 5, 6, 0, 3, NA, 5, 4, 5, 0, NA, 9, 10, 5, 4,...
## $ Enjoy_Wk4 <dbl> NA, 0, 0, NA, NA, NA, 0, NA, 4, 2, NA, NA, NA, ...
## $ Enjoy_Wk8 <dbl> NA, 5, 10, 1, NA, NA, 0, 0, 0, 0, NA, NA, NA, N...
## $ Enjoy_Wk12 <dbl> NA, 4, 10, 5, 10, NA, NA, NA, 4, 0, NA, NA, NA,...
## $ Enjoy_Wk24 <dbl> NA, 4, NA, 4, NA, NA, NA, 10, 5, 4, NA, NA, NA,...
## $ Enjoy_Wk48 <dbl> NA, 8, NA, 7, NA, NA, NA, 5, NA, 4, NA, NA, NA,...head(bpi)tail(bpi)Pain severity scale (PSS)
Clean data
# Generate data
bpi_pss <- bpi %>%
# Select columns
select(ID, Site, Group, Sex,
starts_with('Worst'), starts_with('Least'),
starts_with('Avg'), starts_with('Now')) %>%
# Convert to long format
tidyr::gather(key = pain_question,
value = pain_intensity,
-ID, - Site, - Group, -Sex) %>%
# Seperate pain_question into constituent parts
tidyr::separate(col = pain_question,
into = c('pain_question', 'time_point')) %>%
# Calculate PSS
group_by(ID, time_point) %>%
mutate(PSS = round(mean(pain_intensity, na.rm = TRUE), 1),
PSS = ifelse(is.nan(PSS),
yes = NA,
no = PSS)) %>%
# Order the time points
ungroup() %>%
mutate(time_point = factor(time_point,
levels = c('BL', 'Wk4', 'Wk8',
'Wk12', 'Wk24', 'Wk48'),
ordered = TRUE))Plots
# Create spaghetti plot using all PSS data, to allow for faceting.
# Change time point to numerical so as to space appropriately in graph
# First temporarily convert values from factor to character
bpi_pss <- mutate(bpi_pss,
time_point = as.character(time_point))
# Replace values
bpi_pss[bpi_pss=="BL"]<- "BL0"
# Make a copy of bpi_pss
bpi_pss2 <- bpi_pss
# Separate after 2 characters
bpi_pss <- tidyr::separate(data = bpi_pss,
col = time_point,
into = c('Text', 'Week'), 2)
# Convert back into numeric form
bpi_pss <- mutate (bpi_pss,
Week = as.numeric(Week))Spaghetti plots
# Create spaghetti plot
# Try using geom.plot, via tutorial at
# https://stats.idre.ucla.edu/r/faq/how-can-i-visualize-longitudinal-data-in-ggplot2/
# Colour by site
ggplot(data = bpi_pss) +
aes(x = Week,
y = PSS,
group = ID,
colour = Site) +
geom_line()+
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(0,2,4,6,8,10))
# Colour by individual, facet by site
ggplot(data = bpi_pss) +
aes(x = Week,
y = PSS,
colour = ID) +
geom_line() +
facet_grid(Site ~ .) +
labs(title = 'Pain Severity Scale',
subtitle = 'Faceted by study site, coloured by ID')+
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(0,2,4,6,8,10))
# Facet by study site and gender, colour by ID
ggplot(data = bpi_pss) +
aes(x = Week,
y = PSS,
colour = ID) +
geom_line() +
facet_grid(Site ~ Sex) +
labs(title = 'Pain Severity Scale',
subtitle = 'Faceted by study site, coloured by ID')+
theme(legend.position = 'none')+
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(0,2,4,6,8,10))
# Facet by ID and U1 only
bpi_pss_U1 <- bpi_pss %>%
filter(Site == "u1")
ggplot(data = bpi_pss_U1) +
aes(x = Week,
y = PSS,
colour = ID) +
geom_line() +
facet_wrap(facet = ~ID) +
labs(title = 'Pain Severity Scale',
subtitle = 'U1 site only; faceted by ID')+
theme(legend.position = 'none')+
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(0,2,4,6,8,10))
# Facet by individual at each site
## U1
pss_spagID <- bpi_pss %>%
select(-pain_question, -pain_intensity) %>%
unique(.) %>%
group_by(Site, Group) %>%
nest() %>%
mutate(Number = row_number()) %>%
unnest() %>%
nest(-Number) %>%
mutate(plot = map(data,
~ggplot(data = .x) +
aes(x = Week,
y = PSS,
colour = Sex) +
geom_line() +
geom_point(size = 2) +
labs(title = 'Pain severity scale',
subtitle = glue('Site: {str_to_title(unique(.x$Site))} ; Group: {.x$Group}')) +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(0, 2, 4, 6, 8, 10),
limits = c(0, 10)) +
facet_wrap(facets = ~ID, ncol = 4) +
theme(legend.position = 'top')))
pss_spagID$plot## [[1]]
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Summary plots
## PSS: Faceted by study site, coloured by group
ggplot(data = bpi_pss2) +
aes(x = time_point,
y = PSS,
colour = Group,
fill = Group) %>%
geom_boxplot(alpha = 0.6) +
facet_grid(Site ~ .) +
labs(title = 'Pain Severity Scale (PSS)',
subtitle = 'Faceted by study site, coloured by group')
## PSS: Faceted by study group, coloured by sex'
ggplot(data = bpi_pss2) +
aes(x = time_point,
y = PSS,
colour = Sex,
fill = Sex) %>%
geom_boxplot(alpha = 0.6) +
facet_grid(Group ~ .) +
labs(title = 'Pain Severity Scale (PSS)',
subtitle = 'Faceted by study group, coloured by sex')
## PSS: Faceted by study site and group, coloured by sex
ggplot(data = bpi_pss2) +
aes(x = time_point,
y = PSS,
colour = Sex,
fill = Sex) %>%
geom_boxplot(alpha = 0.6) +
facet_grid(Site ~ Group) +
labs(title = 'Pain Severity Scale (PSS)',
subtitle = 'Faceted by study site and group, coloured by sex')
## PSS: Women only
ggplot(data = filter(bpi_pss2, Sex == 'female')) +
aes(x = time_point,
y = PSS) %>%
geom_boxplot(alpha = 0.6) +
labs(title = 'Pain Severity Scale (PSS)',
subtitle = 'Women only')
## PSS: Women only (excluding U1)
ggplot(data = filter(bpi_pss2, Sex == 'female' & Site != 'u1')) +
aes(x = time_point,
y = PSS) %>%
geom_boxplot(alpha = 0.6) +
labs(title = 'Pain Severity Scale (PSS)',
subtitle = 'Women only (excluding u1)')
Pain interference scale (PIS)
Clean data
# Generate data
bpi_pis <- bpi %>%
# Select columns
select(ID, Site, Group, Sex,
starts_with('Activ'), starts_with('Mood'),
starts_with('Walking'), starts_with('Work'),
starts_with('Rel'), starts_with('Sleep'),
starts_with('Enjoy')) %>%
select(-contains('Relief')) %>%
# Convert to long format
tidyr::gather(key = interference_question,
value = interference_intensity,
-ID, -Site, -Group, -Sex) %>%
# Separate pain_question into constituent parts
tidyr::separate(col = interference_question,
into = c('interference_question', 'time_point')) %>%
# Calculate PIS
group_by(ID, time_point) %>%
mutate(PIS = round(mean(interference_intensity, na.rm = TRUE), 1),
PIS = ifelse(is.nan(PIS),
yes = NA,
no = PIS)) %>%
# Order the time points
ungroup() %>%
mutate(time_point = factor(time_point,
levels = c('BL', 'Wk4', 'Wk8',
'Wk12', 'Wk24', 'Wk48'),
ordered = TRUE))Plots
# First temporarily convert values from factor to character
bpi_pis <- mutate (bpi_pis,
time_point = as.character(time_point))
# Replace values
bpi_pis[bpi_pis=="BL"]<- "BL0"
# Make a copy of bpi_pis
bpi_pis2 <- bpi_pis
# Separate after 2 characters
bpi_pis <- tidyr::separate(data = bpi_pis,
col = time_point,
into = c('Text', 'Week'), 2)
# Convert back into numeric form
bpi_pis <- mutate (bpi_pis,
Week = as.numeric(Week))Spaghetti plots
# Colour by site
ggplot(data = bpi_pis, aes(x = Week, y = PIS, group = ID, colour = Site))+
geom_line()+
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(0,2,4,6,8,10))
# Colour by individual, facet by site
ggplot(data = bpi_pis) +
aes(x = Week,
y = PIS,
colour = ID) %>%
geom_line() +
facet_grid(Site ~ .) +
labs(title = 'Pain Interference Scale',
subtitle = 'Faceted by study site, coloured by ID')+
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(0,2,4,6,8,10))
# Facet by study site and gender, colour by ID
ggplot(data = bpi_pis) +
aes(x = Week,
y = PIS,
colour = ID) %>%
geom_line() +
facet_grid(Site ~ Sex) +
labs(title = 'Pain Interference Scale',
subtitle = 'Faceted by study site, coloured by ID')+
theme(legend.position = 'none')+
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(0,2,4,6,8,10))
Summary plots
## PIS: Faceted by study site, coloured by group
ggplot(data = bpi_pis2) +
aes(x = time_point,
y = PIS,
colour = Group,
fill = Group) %>%
geom_boxplot(alpha = 0.6) +
facet_grid(Site ~ .) +
labs(title = 'Pain Interference Scale (PIS)',
subtitle = 'Faceted by study site, coloured by group')
## PIS: Faceted by study group, coloured by sex'
ggplot(data = bpi_pis2) +
aes(x = time_point,
y = PIS,
colour = Sex,
fill = Sex) %>%
geom_boxplot(alpha = 0.6) +
facet_grid(Group ~ .) +
labs(title = 'Pain Interference Scale (PIS)',
subtitle = 'Faceted by study group, coloured by sex')
## PIS: Faceted by study site and group, coloured by sex
ggplot(data = bpi_pis2) +
aes(x = time_point,
y = PIS,
colour = Sex,
fill = Sex) %>%
geom_boxplot(alpha = 0.6) +
facet_grid(Site ~ Group) +
labs(title = 'Pain Interference Scale (PIS)',
subtitle = 'Faceted by study site and group, coloured by sex')
## PIS: Women only
ggplot(data = filter(bpi_pis2, Sex == 'female')) +
aes(x = time_point,
y = PIS) %>%
geom_boxplot(alpha = 0.6) +
labs(title = 'Pain Interference Scale (PIS)',
subtitle = 'Women only')
## PIS: Women only (excluding U1)
ggplot(data = filter(bpi_pis2, Sex == 'female' & Site != 'u1')) +
aes(x = time_point,
y = PIS) %>%
geom_boxplot(alpha = 0.6) +
labs(title = 'Pain Interference Scale (PIS): Women only',
subtitle = 'Excluding U1')
Session information
sessionInfo()## R version 3.4.2 (2017-09-28)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] bindrcpp_0.2 ggplot2_2.2.1 glue_1.1.1 stringr_1.2.0 tidyr_0.7.2
## [6] purrr_0.2.4 dplyr_0.7.4
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.13 knitr_1.17 bindr_0.1 magrittr_1.5
## [5] hms_0.3 tidyselect_0.2.2 munsell_0.4.3 colorspace_1.3-2
## [9] R6_2.2.2 rlang_0.1.2 plyr_1.8.4 tools_3.4.2
## [13] grid_3.4.2 gtable_0.2.0 htmltools_0.3.6 lazyeval_0.2.0
## [17] yaml_2.1.14 assertthat_0.2.0 rprojroot_1.2 digest_0.6.12
## [21] tibble_1.3.4 reshape2_1.4.2 readr_1.1.1 evaluate_0.10.1
## [25] rmarkdown_1.6 labeling_0.3 stringi_1.1.5 compiler_3.4.2
## [29] scales_0.5.0 backports_1.1.1 jsonlite_1.5 pkgconfig_2.0.1